Authors : Saroj Kumar Behera

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/97up3jhr

Scribd : https://tinyurl.com/e872exck

DOI : https://doi.org/10.38124/ijisrt/25nov108

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Abstract : Through the integration of mechanical tracking and connected intelligence, IoT-based solar tracking systems can significantly enhance photovoltaic energy capture and enable smarter operations and maintenance. This review covers recent developments in single- and dual-axis trackers, sensor and actuator hardware, and edge-to–cloud IoT stacks supporting real-time telemetry, remote control of devices, as well as predictive maintenance. Both are supported by open source software and cloud computing technologies. In this paper, we compare open-loop astronomical algorithms and AIoT controllers with sensor-driven closed-Loop approaches (e.g, LDR/photodiaode feedback), considering the tradeoffs between point accuracy, "actuation energy", and lifecycle costs. The standard outcome of performance analyses demonstrates that fixed mounts offer an average increase of 15-30% in single-axis performance (with bifacial modules providing additional benefits), while IoT-enabled analytics reduce downtime and enhance O&M efficiency by anomaly detection and targeted interventions. Utility-scale farms, residential off-grid systems, and agri-food applications such as solar-powered drying and cold storage are all possible applications where increased availability directly reduces post-harvest loss. There are still some obstacles to overcome, including mechanical reliability, site-specific economics, and cybersecurity risks caused by networked control.

Keywords : IoT, Solar Tracking, Photovoltaic Optimization, Edge AI, Predictive Maintenance.

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